EE209AS (Fall 2018)
Problem set 3 Instructor: Prof. Ankur Mehta
Team Member: Ray Lin, Pengrui Quan
Objective
To explore Kalman Filtering (KF) to estimate the state of a simple two-wheeled robot
Experiment results
Estimated location and orientation with low uncertainty initialized. For more intuitive illustration, please refer to our animation Known inital position.mp4 for the distribution of random varaibles.
Estimated location and orientation with high uncertainty initialized. For more intuitive illustration, please refer to our animation Unknown inital position.mp4 for the distribution of random varaibles.
Instructions on running the code:
please go to the main function of the code, initialize the characteristic of the robot with robot(state_mean, state_cov, gt_state), and call test functions, such as test_case2(robot), test_case3(robot), and test_case4(robot)